This document has code embedded throughout. In the next section we will create a visualization using the already loaded dataset cryptodata:
datatable(cryptodata, rownames = FALSE,
options(list(lengthMenu = c(4, 5, 6))))
Overview
Price Chart
Interactive Chart
Python Code Example
import pandas as pd # Show the R data from Python r.cryptodata
## pair symbol ask_1_price date_time_utc ## 0 ETHUSD ETH 591.306 2020-12-08 00:00:01 ## 1 ETHUSD ETH 590.409 2020-12-08 01:00:01 ## 2 ETHUSD ETH 589.191 2020-12-08 02:00:01 ## 3 ETHUSD ETH 589.218 2020-12-08 03:00:01 ## 4 ETHUSD ETH 588.182 2020-12-08 04:00:01 ## ... ... ... ... ... ## 2015 ETHUSD ETH 355.851 2020-09-09 20:00:38 ## 2016 ETHUSD ETH 352.805 2020-09-09 21:00:39 ## 2017 ETHUSD ETH 352.934 2020-09-09 22:00:39 ## 2018 ETHUSD ETH 355.471 2020-09-09 23:00:38 ## 2019 ETHUSD ETH 357.844 NaT ## ## [2020 rows x 4 columns]
One more Python example
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import numpy as np
# Create a new field based on the price value:
df['price_percentile'] = np.where(df['ask_1_price'] > np.percentile(df['ask_1_price'], 50),
'upper 50th percentile of prices',
'lower 50th percentile of prices')
# Show modified dataframe:
df[['symbol', 'ask_1_price', 'price_percentile']]
## symbol ask_1_price price_percentile ## 0 ETH 591.306 upper 50th percentile of prices ## 1 ETH 590.409 upper 50th percentile of prices ## 2 ETH 589.191 upper 50th percentile of prices ## 3 ETH 589.218 upper 50th percentile of prices ## 4 ETH 588.182 upper 50th percentile of prices ## ... ... ... ... ## 2015 ETH 355.851 lower 50th percentile of prices ## 2016 ETH 352.805 lower 50th percentile of prices ## 2017 ETH 352.934 lower 50th percentile of prices ## 2018 ETH 355.471 lower 50th percentile of prices ## 2019 ETH 357.844 lower 50th percentile of prices ## ## [2020 rows x 3 columns]